文章目录

    • 流程图
    • 相机标定
    • 立体匹配
    • 效果
      • 1.原图像
      • 2.深度图
      • 3.代码链接

流程图

相机标定

参考链接:

import cv2import numpy as np# -----------------------------------双目相机的基本参数---------------------------------------------------------#   left_camera_matrix          左相机的内参矩阵#   right_camera_matrix         右相机的内参矩阵##   left_distortion             左相机的畸变系数    格式(K1,K2,P1,P2,0)#   right_distortion            右相机的畸变系数# -------------------------------------------------------------------------------------------------------------# 左镜头的内参,如焦距left_camera_matrix = np.array([[516.5066236,-1.444673028,320.2950423],[0,516.5816117,270.7881873],[0.,0.,1.]])right_camera_matrix = np.array([[511.8428182,1.295112628,317.310253],[0,513.0748795,269.5885026],[0.,0.,1.]])# 畸变系数,K1、K2、K3为径向畸变,P1、P2为切向畸变left_distortion = np.array([[-0.046645194,0.077595167, 0.012476819,-0.000711358,0]])right_distortion = np.array([[-0.061588946,0.122384376,0.011081232,-0.000750439,0]])# 旋转矩阵R = np.array([[0.999911333,-0.004351508,0.012585312],              [0.004184066,0.999902792,0.013300386],              [-0.012641965,-0.013246549,0.999832341]])# 平移矩阵T = np.array([-120.3559901,-0.188953775,-0.662073075])size = (640, 480)R1, R2, P1, P2, Q, validPixROI1, validPixROI2 = cv2.stereoRectify(left_camera_matrix, left_distortion,                                                                  right_camera_matrix, right_distortion, size, R,                                                                  T)# 校正查找映射表,将原始图像和校正后的图像上的点一一对应起来left_map1, left_map2 = cv2.initUndistortRectifyMap(left_camera_matrix, left_distortion, R1, P1, size, cv2.CV_16SC2)right_map1, right_map2 = cv2.initUndistortRectifyMap(right_camera_matrix, right_distortion, R2, P2, size, cv2.CV_16SC2)print(Q)

cv2.stereoRectify()函数

  • 示例:R1, R2, P1, P2, Q, validPixROI1, validPixROI2 = cv2.stereoRectify(left_camera_matrix, left_distortion,right_camera_matrix, right_distortion, size, R, T)
  • 作用:为每个摄像头计算立体校正的映射矩阵R1, R2, P1, P2
  • 参数:
    1. left_camera_matrix:左相机内参
    2. left_distortion:左相机畸变系数
    3. right_camera_matrix:右相机内参
    4. right_distortion:右相机畸变系数
    5. size:单边相机的图片分辨率
    6. R:旋转矩阵
    7. T:平移矩阵
  • 返回值:
    1. R1, R2:R1-输出矩阵,第一个摄像机的校正变换矩阵(旋转变换);R2-输出矩阵,第二个摄像机的校正变换矩阵(旋转变换)
    2. P1, P2:P1-输出矩阵,第一个摄像机在新坐标系下的投影矩阵;P2-输出矩阵,第二个摄像机在新坐标系下的投影矩阵

立体匹配

import numpy as npimport cv2import randomimport math# 加载视频文件capture = cv2.VideoCapture("./car.avi")WIN_NAME = 'Deep disp'cv2.namedWindow(WIN_NAME, cv2.WINDOW_AUTOSIZE)# 读取视频fps = 0.0ret, frame = capture.read()while ret:    # 开始计时    t1 = time.time()    # 是否读取到了帧,读取到了则为True    ret, frame = capture.read()    # 切割为左右两张图片    frame1 = frame[0:480, 0:640]    frame2 = frame[0:480, 640:1280]    # 将BGR格式转换成灰度图片,用于畸变矫正    imgL = cv2.cvtColor(frame1, cv2.COLOR_BGR2GRAY)    imgR = cv2.cvtColor(frame2, cv2.COLOR_BGR2GRAY)    # 重映射,就是把一幅图像中某位置的像素放置到另一个图片指定位置的过程。    # 依据MATLAB测量数据重建无畸变图片,输入图片要求为灰度图    img1_rectified = cv2.remap(imgL, left_map1, left_map2, cv2.INTER_LINEAR)    img2_rectified = cv2.remap(imgR, right_map1, right_map2, cv2.INTER_LINEAR)    # 转换为opencv的BGR格式    imageL = cv2.cvtColor(img1_rectified, cv2.COLOR_GRAY2BGR)    imageR = cv2.cvtColor(img2_rectified, cv2.COLOR_GRAY2BGR)    # ------------------------------------SGBM算法----------------------------------------------------------    #   blockSize                   深度图成块,blocksize越低,其深度图就越零碎,0<blockSize<10    #   img_channels                BGR图像的颜色通道,img_channels=3,不可更改    #   numDisparities              SGBM感知的范围,越大生成的精度越好,速度越慢,需要被16整除,如numDisparities    #                               取16、32、48、64等    #   mode                        sgbm算法选择模式,以速度由快到慢为:STEREO_SGBM_MODE_SGBM_3WAY、    #                               STEREO_SGBM_MODE_HH4、STEREO_SGBM_MODE_SGBM、STEREO_SGBM_MODE_HH。精度反之    # ------------------------------------------------------------------------------------------------------    blockSize = 8    img_channels = 3    stereo = cv2.StereoSGBM_create(minDisparity=1,                                   numDisparities=64,                                   blockSize=blockSize,                                   P1=8 * img_channels * blockSize * blockSize,                                   P2=32 * img_channels * blockSize * blockSize,                                   disp12MaxDiff=-1,                                   preFilterCap=1,                                   uniquenessRatio=10,                                   speckleWindowSize=100,                                   speckleRange=100,                                   mode=cv2.STEREO_SGBM_MODE_HH)    # 计算视差    disparity = stereo.compute(img1_rectified, img2_rectified)    # 归一化函数算法,生成深度图(灰度图)    disp = cv2.normalize(disparity, disparity, alpha=0, beta=255, norm_type=cv2.NORM_MINMAX, dtype=cv2.CV_8U)    # 生成深度图(颜色图)    dis_color = disparity    dis_color = cv2.normalize(dis_color, None, alpha=0, beta=255, norm_type=cv2.NORM_MINMAX, dtype=cv2.CV_8U)    dis_color = cv2.applyColorMap(dis_color, 2)    # 计算三维坐标数据值    threeD = cv2.reprojectImageTo3D(disparity, Q, handleMissingValues=True)    # 计算出的threeD,需要乘以16,才等于现实中的距离    threeD = threeD * 16    # 鼠标回调事件    cv2.setMouseCallback("depth", onmouse_pick_points, threeD)    #完成计时,计算帧率    fps = (fps + (1. / (time.time() - t1))) / 2    frame = cv2.putText(frame, "fps= %.2f" % (fps), (0, 40), cv2.FONT_HERSHEY_SIMPLEX, 1, (0, 255, 0), 2)    cv2.imshow("depth", dis_color)    cv2.imshow("left", frame1)    cv2.imshow(WIN_NAME, disp)  # 显示深度图的双目画面    # 若键盘按下q则退出播放    if cv2.waitKey(20) & 0xff == ord('q'):        break# 释放资源capture.release()# 关闭所有窗口cv2.destroyAllWindows()
  • img1_rectified = cv2.remap(imgL, left_map1, left_map2, cv2.INTER_LINEAR):重映射,即把一幅图像内的像素点放置到另外一幅图像内的指定位置,俗称“拼接”

    我们可以通过cv.remap()函数来将img2映射到img1对应位置上并合成

  • cv2.StereoSGBM_create()函数为opencv集成的算法;我们只需关注blockSize。 使用方法为:

    其中,调小numDisparities会降低精度,但提高速度。注意:numDisparities需能被16整除

    mode可以设置为STEREO_SGBM_MODE_SGBM_3WAY ,STEREO_SGBM_MODE_HH, STEREO_SGBM_MODE_SGBM, STEREO_SGBM_MODE_HH4四种模式,它们的精度和速度呈反比,可根据情况来选择不同的模式.STEREO_SGBM_MODE_HH4的速度最快,STEREO_SGBM_MODE_HH的精度最好

效果

1.原图像

2.深度图

3.代码链接

https://github.com/yzfzzz/Stereo-Detection